Wiener Postfilter Research Articles

Random Matrix Theory-Based Wiener Postfilter Combined With Eigenspace and Spatial Coherence-Based Minimum Variance Beamformer for Coherent Plane-Wave Adaptive Compounding

Random Matrix Theory-Based Wiener Postfilter Combined With Eigenspace and Spatial Coherence-Based Minimum Variance Beamformer for Coherent Plane-Wave Adaptive Compounding

Improved Ultrafast Power Doppler Imaging Using United Spatial-Angular Adaptive Scaling Wiener Postfilter.

Ultrafast power Doppler imaging (uPDI) using high-frame-rate plane-wave transmission is a new microvascular imaging modality that offers high Doppler sensitivity. However, due to the unfocused transmission of plane waves, the echo signal is subject to interference from noise and clutter, resulting in a low signal-to-noise ratio (SNR) and poor image quality. Adaptive beamforming techniques are effective in suppressing noise and clutter for improved image quality. In this study, an adaptive beamformer based on a united spatial-angular adaptive scaling Wiener (uSA-ASW) postfilter is proposed to improve the resolution and contrast of uPDI. In the proposed method, the signal power and noise power of the Wiener postfilter are estimated by uniting spatial and angular signals, and a united generalized coherence factor (uGCF) is introduced to dynamically adjust the noise power estimation and enhance the robustness of the method. Simulation and in vivo data were used to verify the effectiveness of the proposed method. The results show that the uSA-ASW can achieve higher resolution and significant improvements in image contrast and background noise suppression compared with conventional delay-and-sum (DAS), coherence factor (CF), spatial-angular CF (SACF), and adaptive scaling Wiener (ASW) postfilter methods. In the simulations, uSA-ASW improves contrast-to-noise ratio (CNR) by 34.7 dB (117.3%) compared with DAS, while reducing background noise power (BNP) by 52 dB (221.4%). The uSA-ASW method provides full-width at half-maximum (FWHM) reductions of [Formula: see text] (59.5%) and [Formula: see text] (56.9%), CNR improvements of 25.6 dB (199.9%) and 42 dB (253%), and BNP reductions of 46.1 dB (319.3%) and 12.9 dB (289.1%) over DAS in the experiments of contrast-free human neonatal brain and contrast-free human liver, respectively. In the contrast-free experiments, uSA-ASW effectively balances the performance of noise and clutter suppression and enhanced microvascular visualization. Overall, the proposed method has the potential to become a reliable microvascular imaging technique for aiding in more accurate diagnosis and detection of vascular-related diseases in clinical contexts.

Supervised speech separation combined with adaptive beamforming

Microphone arrays are a powerful tool for ambient noise suppression. A multi-channel minimum mean square error (MMSE) solution can be factorized into a minimum variance distortionless response beamformer (MVDR) followed by a single-channel Wiener post-filter. MVDR beamformer, as well as its equivalent form of generalized sidelobe canceller (GSC), often does not provide sufficient noise reduction due to its limited ability to reduce diffuse noise and reverberation. Steering and calibration errors also degrade the performance of both MVDR and GSC beamformers. Post-filter can be realized by any single-channel noise reduction method. A modern and promising approach for single-channel noise reduction is formulated as a supervised speech separation (SSS) in which a supervised learning algorithm, typically a deep neural network (DNN), is trained to learn a mapping from the noisy features to a time-frequency representation of the target of interest. In this paper, we combined SSS and adaptive beamforming approaches. Adaptive beamforming is realized by simplified GSC (S-GSC) whose equivalence with MVDR beamformer is also proved in the paper. In the proposed S-GSC beamformer, the conventional beamformer is replaced by the central microphone signal. Steering towards the target speaker needs no direction of arrival (DOA) estimation. Trained DNN of the SSS module estimates ideal ratio mask (IRM) which is used for adaptation of the blocking matrix, calibration of the microphones, adaptation for the adaptive noise canceller, and the post-filtering. The proposed method was tested on 720 utterances of the TIMIT database used as target speech. The reverberant room was simulated by acoustic impulse responses recorded in the real room. Performance analysis was carried out with PESQ, STOI, and SDR measures. The test results showed that the proposed combined method outperforms the individual SSS and S-GSC methods.

Generalized sidelobe canceler beamforming combined with Eigenspace-Wiener postfilter for medical ultrasound imaging.

BACKGROUND: The beamforming algorithm is key to the image quality of the medical ultrasound system. The generalized sidelobe canceler (GSC) beamforming can improve the image quality in lateral resolution, but the contrast is not improved correspondingly.OBJECTIVE: In our research, we try to optimize the generalized sidelobe canceler to obtain images that achieve an improvement in both lateral resolution and contrast.METHODS: We put forward a new beamforming algorithm which combines the generalized sidelobe canceler and Eigenspace-Wiener postfilter. According to eigenspace decomposition of the covariance matrix of the received data, the components of the Wiener postfilter can be calculated from the signal matrix and the noise matrix. Then, the adaptive weight vector of GSC is further constrained by the Eigenspace-Wiener postfilter, which make the output energy of the receiving array closer to the desired signal than the conventional GSC output.RESULTS: We compare the new beamforming algorithm with delay-and-sum (DS) beamforming, synthetic aperture (SA) beamforming, and GSC beamforming using the simulated and experimental data sets. The quantitative results show that our method reduces the FWHM by 85.5%, 80.5%, and 38.9% while improving the CR by 123.6%, 47.7%, 84.4% on basis of DS, SA, and GSC beamforming, respectively.CONCLUSIONS: The new beamforming algorithm can obviously improve the imaging quality of medical ultrasound imaging systems in both lateral resolution and contrast.

Ultrasound far-focused pixel-based imaging using Wiener postfilter scaled by adjustable zero-cross factor

Synthetic aperture (SA) imaging can provide a uniform lateral resolution but an insufficient signal-to-noise ratio (SNR). SA method with bidirectional pixel-based focusing (SA-BiPBF) has the ability to obtain a higher quality image than conventional SA imaging. In this paper, an enhanced SA-BiPBF named full aperture received far-focused pixel-based (FrFPB) is firstly proposed to obtain a high resolution image. An adjustable zero-cross factor scaled Wiener postfilter (AZFsW) is then implemented in FrFPB for improving contrast ratio (CR). The adjustable zero-cross factor is calculated using the polarity of echo signals sequence with an adjustable coefficient σ to estimate the signal coherence, and it is combined with Wiener postfilter to obtain a good capability of noise reduction and background speckle pattern preservation. Simulation and experiments have been conducted to evaluate the imaging performance of the proposed methods. Results show that FrFPB can obviously improve the resolution in comparison with SA-BiPBF, and contrast-to-noise ratio (CNR) and speckle signal-to-noise ratio (sSNR) are retained. In addition, AZFsW can achieve a much higher CR than SA-BiPBF. When σ is 0.6, the CR improvement is 96.7% in simulation, 78.7% in phantom experiment, and 49.2% in in-vivo experiment. To evaluate the imaging performance of AZFsW, coherence factor, conventional Wiener postfilter, and scaled Wiener postfilter are implemented. The imaging results show that when σ is in the range of [0.6, 0.7], AZFsW exhibits a satisfying comprehensive imaging performance.

United Wiener postfilter for plane wave compounding ultrasound imaging

Plane wave compounding (PWC) is a valid method for ultrafast ultrasound imaging. Its imaging quality depends on the beamforming method. The coherence factor (CF) and Wiener postfilter are effective signal processing schemes for aberration correction. However, the CF usually causes over-suppression and brings artifacts. Additionally, the conventional CF and Wiener postfilter cannot fully utilize the spatial coherence in the PWC, which limits the imaging performance and increases the computation. In this paper, we propose a united Wiener postfilter specially for the PWC. The signal and noise power are both estimated through the echo signal matrix, rather than array signal vectors. The method also accords with the theoretical relationship between the CF and Wiener. To evaluate the performance of the proposed method, we conduct simulations, phantom and in vivo experiments and make comparisons with the delay-and-sum (DAS), the CF, the generalized coherence factor (GCF), the conventional Wiener and the scaled Wiener beamformers. Results indicate that our method can offer the better resolution and contrast than the DAS and Wiener. It also solves the over-suppression drawback of the CF. Specifically, the contrast ratio and contrast-to-noise ratio achieve 26.7% and 25.2% improvements in simulations, 28.7% and 32.4% in phantom experiments, respectively. The proposed method also performs well in terms of the speckle signal-to-noise ratio and the generalized contrast-to-noise ratio. Consequently, we believe that the proposed method is effective in enhancing the imaging quality of the PWC.

Subspace-Based Blood Power Spectral Capon Combined with Wiener Postfilter to Provide a High-Quality Velocity Waveform with Low Mathematical Complexity

In Doppler analysis, the power spectral density (PSD), which accounts for the axial velocity distribution of the blood scatterers, is estimated. The conventional spectral estimator is Welch's method, which suffers from frequency leakage at small observation window length. The performance of adaptive techniques such as blood power Capon (BPC) has been promising at the cost of higher computation complexity. Reducing the computational complexity while retaining the benefits of BPC would be necessary for real-time implementation. The purpose of the work described here was to investigate whether it is possible to decrease the computation load in BPC and still obtain acceptable results. The computation complexity in BPC is owing primarily to the matrix inversion required for computing the PSD estimate. We here propose the subspace blood power Capon technique, which employs a data covariance matrix with reduced number of rows in estimation of the weight vector. In maximum velocity estimation in the spectra, the signal noise slope intersection envelop estimator that makes use of the integrated power spectrum is employed. The evaluations are made based on both simulated and in vivo data. The results indicate that it is possible to reduce the order of complexity to almost 12.25% at the cost of 2.31% and 2.24% increases in the relative standard deviation and relative bias of the estimates. Moreover, the Wiener post-filter as a post-weighting factor, which will be multiplied by the final weight vector of the spectral estimator, estimates the power of the desired signal and the power of the interference plus noise to improve the contrast. The proposed estimator has exhibited a promising performance at beam-to-flow angles of 45°, 60° and 75°. Furthermore, the robust performance of the proposed estimator against variation in the flow rate is also documented.

Microphone Array Wiener Post Filtering Using Monotone Operator Splitting

For array-based acoustic source enhancement, variants of multi-channel Wiener filters are commonly used. The approach includes a Wiener post-filter that requires the simultaneous estimation of the power spectral density (PSD) of the target source and of noise sources for each time-frame. Conventional methods generally do not exploit prior knowledge , such as sparsity of the source, in solving this simultaneous estimation problem. We show that, for common scenarios, the simultaneous PSD estimation with consideration of prior knowledge can be formulated as a convex optimization problem with linear constraints. We use monotone operator splitting (MOS) to solve the constrained optimization problem. Our experiments confirm that the proposed method improves the accuracy of the noise PSD estimation, and that the resulting enhanced target signal is of higher quality.

Adaptive scaling Wiener postfilter using generalized coherence factor for coherent plane-wave compounding

This paper proposes an adaptive scaling Wiener postfilter (AScW) for coherent plane-wave compounding (CPWC) to improve the image quality. AScW introduces an adaptive scale factor dependent on the signal incoherence to maintain the performance balance between good noise suppression and good robustness. AScW utilizes several plane waves with a small angular difference to calculate generalized coherence factor (GCF) for the estimation of signal incoherence and noise power. And a standard depth parameter is used to further adjust the beamforming performance of AScW and improve speckle quality. The proposed method was tested on datasets acquired from simulation, experimental phantom and in-vivo study provided by the Plane-wave Imaging Challenge in Medical Ultrasound (PICMUS). Results show that AScW applied to CPWC can provide a good speckle quality and lateral resolution, while attaining a satisfying contrast. AScW can achieve maximal improvements of speckle signal-to-noise ratio (sSNR) by 36.4% (simulation) and 44.8% (experiment) compared with GCF, while 14.4% (simulation) and 12.5% (experiment) compared with the scaled Wiener postfilter (ScW). And the maximal lateral full width at half maximum (FWHM) improvements are 51.9% upon GCF and 53.7% upon ScW. Meanwhile, the contrast of AScW is comparable to that of GCF. From the results of in-vivo study, AScW also shows its clinical application potential for the visualization of anatomical structures and hyper echoic structures in ultrasound imaging.

Reverberation suppression algorithm for LS beamforming based on Wiener post-filter

Reverberation suppression algorithm for LS beamforming based on Wiener post-filter

Maximum-Likelihood Approach With Bayesian Refinement for Multichannel-Wiener Postfiltering

Multichannel signal enhancement is an important task, e.g., in modern speech communication. It can be approached in a mean-square error sense by multichannel Wiener filtering, which is often implemented as a cascade of spatial beamforming and spectral enhancement postfiltering. In this paper, we propose a novel Bayesian perspective on the problem of postfilter adaptation. Specifically, we present a two-step procedure for postfilter estimation. We first derive maximum-likelihood (ML) optimal estimators of the target signal and noise power spectral densities (PSD) employing the high-dimensional receiver data in conjunction with blindly estimated channels. When constructing a Wiener postfilter for noisy speech based on the respective speech and noise PSD estimates, this ML-based postfilter is capable of performing good speech enhancement, while leaving musical noise. Hence, we extend to a Bayesian estimation approach in a second step. By statistically modeling the ML-based postfilter estimation and further proposing a prior for the ideal Wiener postfilter, we derive a Bayesian refinement of the ML-based postfilter. This refinement then solely depends on the ML-based postfilter and is independent of the underlying high-dimensional data. Our Bayesian refinement is therefore implemented as a lightweight table look-up. Both proposed postfilters perform well in terms of segmental signal-to-noise ratio and perceptual evaluation of speech quality in various acoustical setups with nonstationary noise. The Bayes-refined postfilter offers consistent improvement over the ML-based postfilter and a single-channel speech enhancement baseline.

Performance of coherence based adaptive sonar beamformers

Recent progress in hardware and computing power has lead to an increased interest in adaptive beamforming for sonar imaging. Several algorithms that for each pixel scale the original sonar image with a coherence estimate of the received data have been proposed. All of these algorithms claim to improve both contrast and resolution. Close examination of the claimed improvement reveals that parts of the reported effects stem from stretching of the dynamic range in the images. This effect is generally unwanted as it can be obtained without extra computation. Of importance is the actual improvement stemming from coherence scaling. In this work we examine the performance of several different coherence based adaptive beamformers (Coherence factor (CF), Generalized CF, Scaled CF, Phase CF, Spatio-Temporally Smoothed CF, and Scaled Wiener postfilter) after removing the unwanted effect from dynamic range stretching. The contrast and resolution performance are compared on simulated and experimental side scan sonar images. Even though dynamic range stretching explains most of the earlier reported improvements, there are differences among the approaches that are worth to take into consideration when choosing which algorithm to apply.

Multispeaker LCMV Beamformer and Postfilter for Source Separation and Noise Reduction

The problem of source separation and noise reduction using multiple microphones is addressed. The minimum mean square error (MMSE) estimator for the multispeaker case is derived and a novel decomposition of this estimator is presented. The MMSE estimator is decomposed into two stages: first, a multispeaker linearly constrained minimum variance (LCMV) beamformer (BF); and second, a subsequent multispeaker Wiener postfilter. The first stage separates and enhances the signals of the individual speakers by utilizing the spatial characteristics of the speakers [as manifested by the respective acoustic transfer functions (ATFs)] and the noise power spectral density (PSD) matrix, while the second stage exploits the speakers’ PSD matrix to reduce the residual noise at the output of the first stage. The output vector of the multispeaker LCMV BF is proven to be the sufficient statistic for estimating the marginal speech signals in both the classic sense and the Bayesian sense. The log spectral amplitude estimator for the multispeaker case is also derived given the multispeaker LCMV BF outputs. The performance evaluation was conducted using measured ATFs and directional noise with various signal-to-noise ratio levels. It is empirically verified that the multispeaker postfilters are beneficial in terms of signal-to-interference plus noise ratio improvement when compared with the single-speaker postfilter.

Coherence factor and Wiener postfilter in synthetic aperture ultrasound imaging.

The coherence factor (CF) and Wiener postfilter methods have been proposed as effective approaches for reducing the output noise of the delay-and-sum (DAS) beamformer in ultrasound imaging. The theoretical framework between them was also established. However, past researches about the CF and Wiener postfilter methods mainly focused on the summation of an array signal. This paper analyzes the CF and Wiener postfilter in the synthetic aperture (SA) imaging mode, where two-dimensional echo data are recorded. Different CF definitions in the SA imaging are first given and the corresponding Wiener postfilter methods are then proposed, including a Wiener postfilter especially for the SA imaging, named as WienerSA. The performances of different CF and Wiener postfilter methods were evaluated on both simulated and experimental SA data. Results showed that the proposed WienerSA outperformed the other Wiener postfilters in reducing the sidelobe noise level. It obtained the highest contrast ratio among the Wiener postfilter methods, which was even higher than some of the CF methods. Meanwhile it could benefit a much higher contrast-to-noise ratio than those CF methods with further suppression of incoherent noises. Consequently, the WienerSA is believed to be a promising approach in enhancing the SA image quality.

Spatial post-filter estimation for speech enhancement in the specific area using a pair of microphone arrays

Suppression methods using the beamformer and the Wiener post-filter by microphone array has been proposed. However, these methods are hard to suppress noise on the environment, such as the desired source is surrounded by noise. We propose the suppression method using a pair of microphone arrays arranged in different positions on the environment, such as the desired source is surrounded by noise. We can get different information of sound by using two microphone arrays. We make the post-filter using its different information of sound. This method enhance the desired source to be the sound source in the specific area. Therefore, we divide the room into nine areas by using two microphone arrays. By estimating the cross spectrum between two microphone arrays of each partition area, we make the post-filter to noise suppression. By the experiment results under actual environment, we confirmed that this method was higher in noise suppression performance than MVDR using one microphone array that is general beamformer.

Signal-Dependent Spatial Filtering Based on Weighted-Orthogonal Beamformers in the Spherical Harmonic Domain

Spatial filtering with microphone arrays is a technique that can be utilized to obtain the signal of a target sound source from a specific direction. Typical approaches in the field of audio underperform in practical environments with multiple sound sources and diffuse sound. In this contribution, we propose a post-filtering technique to suppress the effect of interferers and diffuse sound. The proposed technique utilizes the cross-spectral estimates of the output of two beamformers to formulate a time-frequency soft masker. The beamformers' outputs are used only for parameter estimation and not for generating an audio signal. Two sets of beamformer weights, a constant and an adaptive, are applied to the microphone array signals for the parameter estimation. The weights of the constant beamformer are designed such that they provide a spatially narrow beam pattern that is time and frequency invariant, having a unity gain toward the direction of interest. The weights of the adaptive beamformer are formulated using linearly constrained optimization with the constraint of weighted orthogonality with respect to the constant beamformer weights, as well as the unity gain toward the look direction. The orthogonality constraint provides diffuse sound suppression while the unity gain distortionless response. The cross spectrum of these two beamformers provides the target energy at a given look direction for the post filter. The study focuses on compact microphone arrays with which the typical beamforming techniques feature a tradeoff between noise amplification and spatial selectivity, especially in the low frequency region. The proposed method is evaluated with instrumental measures and listening tests under different reverberation times, in dual and multitalker scenarios. The evaluation shows that the proposed method provides a better performance when compared with a previous state-of-the-art spatial filter based on cross-pattern coherence, a linearly constrained beamformer and a Wiener postfilter.

Speech enhancement with an acoustic vector sensor: an effective adaptive beamforming and post-filtering approach

Speech enhancement has an increasing demand in mobile communications and faces a great challenge in a real ambient noisy environment. This paper develops an effective spatial-frequency domain speech enhancement method with a single acoustic vector sensor (AVS) in conjunction with minimum variance distortionless response (MVDR) spatial filtering and Wiener post-filtering (WPF) techniques. In remote speech applications, the MVDR spatial filtering is effective in suppressing the strong spatial interferences and the Wiener post-filtering is considered as a popular and powerful estimator to further suppress the residual noise if the power spectral density (PSD) of target speech can be estimated properly. With the favorable directional response of the AVS together with the trigonometric relations of the steering vectors, the closed-form estimation of the signal PSDs is derived and the frequency response of the optimal Wiener post-filter is determined accordingly. Extensive computer simulations and a real experiment in an anechoic chamber condition have been carried out to evaluate the performance of the proposed algorithm. Simulation results show that the proposed method offers good ability to suppress the spatial interference while maintaining comparable log spectral deviation and perceptual evaluation of speech quality performance compared with the conventional methods with several objective measures. Moreover, a single AVS solution is particularly attractive for hands-free speech applications due to its compact size.

Adaptive Sonar Imaging Using Aperture Coherence

A class of computationally efficient adaptive imaging methods originating from the coherence factor (CF) has been proposed for improved medical ultrasound imaging. These methods are based on the idea that when steering the receiver toward a point of interest, the backscattered energy from this point exhibits a high degree of aperture coherence, while random noise, multipath interference, and sidelobe energy do not. Aperture coherence can be understood as a normalized measure of the degree of signal variability across the receiver array. This paper presents a study of the use of aperture-coherence-based methods for improved sonar imaging, with particular emphasis on a recently introduced robust implementation known as the scaled Wiener postfilter (SWiP). We show that while the CF has strong noise suppression capabilities and performs well on point targets, it lacks robustness in low signal-to-noise ratio (SNR) scenarios and introduces undesirable artifacts in speckle scenes. The SWiP is closely related to the CF, but contains a single-user-defined parameter, which allows the method to be tuned to suit the application needs. The SWiP can be tuned to offer robustness in a speckle environment such as when imaging the seafloor, or for strong noise suppression capabilities. This makes it a promising method for a wide range of sonar applications. We base our conclusions on simulated data from a constructed speckle scene as well as experimental data from the SX90 fish-finding sonar and sidescan data from the HISAS 1030 sonar. Our results show that the SWiP offers improved edge and shadow definitions and reduced sidelobe levels when compared to the conventional delay-and-sum (DAS) beamformer. These improvements do not compromise the image resolution, and they come at a low computational cost.

Eigenspace-based minimum variance beamformer combined with Wiener postfilter for medical ultrasound imaging

In this paper, a new beamformer which combines the eigenspace-based minimum variance (ESBMV) beamformer with the Wiener postfilter is proposed for medical ultrasound imaging. The primary goal of this work is to further improve the medical ultrasound imaging quality on the basis of the ESBMV beamformer. In this method, we optimize the ESBMV weights with a Wiener postfilter. With the optimization of the Wiener postfilter, the output power of the new beamformer becomes closer to the actual signal power at the imaging point than the ESBMV beamformer. Different from the ordinary Wiener postfilter, the output signal and noise power needed in calculating the Wiener postfilter are estimated respectively by the orthogonal signal subspace and noise subspace constructed from the eigenstructure of the sample covariance matrix.We demonstrate the performance of the new beamformer when resolving point scatterers and cyst phantom using both simulated data and experimental data and compare it with the delay-and-sum (DAS), the minimum variance (MV) and the ESBMV beamformer. We use the full width at half maximum (FWHM) and the peak-side-lobe level (PSL) to quantify the performance of imaging resolution and the contrast ratio (CR) to quantify the performance of imaging contrast. The FWHM of the new beamformer is only 15%, 50% and 50% of those of the DAS, MV and ESBMV beamformer, while the PSL is 127.2dB, 115dB and 60dB lower. What is more, an improvement of 239.8%, 232.5% and 32.9% in CR using simulated data and an improvement of 814%, 1410.7% and 86.7% in CR using experimental data are achieved compared to the DAS, MV and ESBMV beamformer respectively. In addition, the effect of the sound speed error is investigated by artificially overestimating the speed used in calculating the propagation delay and the results show that the new beamformer provides better robustness against the sound speed errors. Therefore, the proposed beamformer offers a better performance than the DAS, MV and ESBMV beamformer, showing its potential in medical ultrasound imaging.

A stereophonic acoustic signal extraction scheme for noisy and reverberant environments

In this contribution, a novel two-channel acoustic front-end for robust automatic speech recognition in adverse acoustic environments with nonstationary interference and reverberation is proposed. From a MISO system perspective, a statistically optimum source signal extraction scheme based on the multichannel Wiener filter (MWF) is discussed for application in noisy and underdetermined scenarios. For free-field and diffuse noise conditions, this optimum scheme reduces to a Delay & Sum beamformer followed by a single-channel Wiener postfilter. Scenarios with multiple simultaneously interfering sources and background noise are usually modeled by a diffuse noise field. However, in reality, the free-field assumption is very weak because of the reverberant nature of acoustic environments. Therefore, we propose to estimate this simplified MWF solution in each frequency bin separately to cope with reverberation. We show that this approach can very efficiently be realized by the combination of a blocking matrix based on semi-blind source separation (‘directional BSS’), which provides a continuously updated reference of all undesired noise and interference components separated from the desired source and its reflections, and a single-channel Wiener postfilter. Moreover, it is shown, how the obtained reference signal of all undesired components can efficiently be used to realize the Wiener postfilter, and at the same time, generalizes well-known postfilter realizations. The proposed front-end and its integration into an automatic speech recognition (ASR) system are analyzed and evaluated in noisy living-room-like environments according to the PASCAL CHiME challenge. A comparison to a simplified front-end based on a free-field assumption shows that the introduced system substantially improves the speech quality and the recognition performance under the considered adverse conditions.